The use of radiation to treat medical conditions comprises a known area of prior art endeavor. For example, radiation therapy comprises an important component of many treatment plans for reducing or eliminating unwanted tumors. Unfortunately, applied radiation does not inherently discriminate between unwanted areas and adjacent healthy tissues, organs, or the like that are desired or even critical to continued survival of the patient. As a result, radiation is ordinarily applied in a carefully administered manner to at least attempt to restrict the radiation to a given target volume.
Treatment plans typically serve to specify any number of operating parameters as pertain to the administration of such treatment with respect to a given patient by use of a given radiation-treatment platform. Such treatment plans are often optimized prior to use. (As used herein, “optimization” will be understood to refer to improving upon a candidate treatment plan without necessarily ensuring that the optimized result is, in fact, the singular best solution.) Many optimization approaches use an automated incremental methodology where various optimization results are calculated and tested in turn using a variety of automatically-modified (i.e., “incremented”) treatment plan optimization parameters.
It is not untypical to employ a dose volume histogram (DVH) estimation when optimizing a radiation treatment plan. The DVH estimation, in turn, is sometimes provided by use of a DVH estimation model. DVH estimation models themselves typically make use of a set of DVH estimation model training features. For example, when the plan requires minimizing radiation exposure to a particular so-called organ at risk, one or more parameters that serve to represent or characterize that organ at risk can serve as such training features. Existing practice tends to require numerous instances where a particular organ at risk structure has been previously characterized to thereby hopefully ensure an accurate model in those regards. Unfortunately, for certain treatment types (for example, using a specific field geometry setting), creating a DVH estimation model for a certain organ at risk may not be able to efficiently and/or effectively utilize a previously-defined set of DVH estimation model training features. For example, even though one might be able to train a good model for all organs typically involved in head-and-neck treatments or prostate treatments, a current set of predefined features nevertheless might not allow a good model to be trained for ribs in a lung-SBRT (Stereotactic Body Radiation Therapy) treatment.
Elements in the figures are illustrated for simplicity and clarity and have not necessarily been drawn to scale. For example, the dimensions and/or relative positioning of some of the elements in the figures may be exaggerated relative to other elements to help to improve understanding of various embodiments of the present teachings. Also, common but well-understood elements that are useful or necessary in a commercially feasible embodiment are often not depicted in order to facilitate a less obstructed view of these various embodiments of the present teachings. Certain actions and/or steps may be described or depicted in a particular order of occurrence while those skilled in the art will understand that such specificity with respect to sequence is not actually required. The terms and expressions used herein have the ordinary technical meaning as is accorded to such terms and expressions by persons skilled in the technical field as set forth above except where different specific meanings have otherwise been set forth herein.